Process Optimization of Bioethanol Production Via Hydrolysis of Switchgrass

Process optimization of bioethanol production via Hydrolysis of Switchgrass

Mariano Martín^a,b, Ignacio E. Grossmann^b[1]

 

mariano.m3@usal.es; grossmann@cmu.edu

 

^a Departamento de Ingeniería química y textil. Universidad de Salamanca. Plz. Caidos 1-5 37008, Salamanca (Spain)

^bDepartment of Chemical Engineering. Carnegie Mellon University 5000 Forbes Avd. Pittsburgh PA 15213 

The Energy act in 2007 established the increase in the production of bioethanol using lignocellulosic raw materials to help reduce the dependence on crude oil. This was based on the fact that among the different possibilities only biomass provides an alternative fuel that can be implemented in the short-term for the transportation sector due to its compatibility with current automobile engines (Cole, 2007) and to the fact that it can take advantage of the existing supply chain of liquid fuels that is already well established. Currently the production of ethanol from lignocellulosic raw materials still faces technical, economic and commercial barriers (Huang, 2008).

Two types of process technologies can be used to transform lignocellulosic raw materials into ethanol. The first one is based on the gasification of the raw material into syngas, which is used to obtain ethanol either via Fischer-Tropsch based catalytic reaction or via fermentation of the syngas (Phillips et al, 2007; Huhnke, 2008;  Piccolo and Bezzo, 2009; Zhu et al, 2009). The second one is based on the hydrolysis of the raw material to break down the physical and chemical structure of the crops to expose the sugars that are fermented to ethanol. Due to its similarity with the current production of ethanol and the expected lower capital cost, this technology has received the attention of many researchers, e.g. Hamelinck et al. (2005); Cardona & Sánchez (2006); Zhang et al. (2009); Keshwani & Cheng (2009).

In this paper we develop a conceptual design for the production of ethanol from lignocellulosic raw materials with hydrolytic pretreatment of the biomass using mathematical programming techniques (Daichendt & Grossmann, 1997, Grossmann et al., 1999). The process consist of three steps, raw material pretreatment to expose the sugars, sugar fermentation to ethanol and ethanol dehydration to fuel grade. We propose a superstructure optimization approach where we first construct a flowsheet embedding the various process units involved in ethanol production considering a number of alternatives for switcgrass pretreatment such as ammonia fiber explosion or dilute acid pretreatment (Sung and Cheng, 2002; Keshwani & Cheng, 2009) and for different technologies that can operated in parallel and/or in sequence for dehydrating the ethanol like rectification, adsorption in corn grits, molecular sieves and pervaporation. These units are interconnected to each other through network flows and other utility streams. The goal is to optimize the structure by minimizing the energy input in the ethanol production process. The optimization of the system is formulated as a mixed-integer nonlinear programming (MINLP) problem, where the model involves a set of constraints representing mass and energy balances for all the units in the system. We then substitute the distillation columns by multieffect column to reduce the consumption on cooling water and steam and we design the optimal heat exchanger network using SYNHEAT. The heat recovery network, together with a modified distillation column design, further reduces the energy consumption in the plant and in turn decreases the unit production cost of ethanol. Finally, we perform an economic evaluation. For the optimal flowsheet we design the optimal water network based on the work by Ahmetovic, et al (2010). First, we identify the sources of water within the process (distillation columns, utilities units), the sinks (fermentor, pretreatment) and the process units (unitities units), and optimize the corresponding superstructure for reuse and recycle of water and treatment units so as to minimize the fresh water consumption.

The results of the optimized conceptual design indicates that under the current yields reported for the pretreatment processes (Sung and Cheng, 2002) the optimal process for the production of ethanol from switchgrass involves the use of dilute acid pretreatment, followed by enzymatic hydrolysis and sugar fermentation and the use of molecular sieves for the final dehydration of ethanol to fuel grade. The predicted production cost of this process is $0.8/gal with an investment cost of $161MM, while the consumption of water is 1.6gal/gal. These results are compared with the themal based processes that requires a higher investment of $335MM, but has a reduced production cost of $0.41/gal and water consumption of 1 gal/gal (Martin & Grossmann, 2011, Martin et al ,2010)

 

References

 

Ahmetovic, E.; Martín, M.; Grossmann, I.E. (2010) “Optimization of Water Consumption in Process industry: Corn – based ethanol case study”  Ind. Eng. Chem Res.  49 (17) 7972- 7982

Cardona, C.A., Sánchez. O.J. (2006) Energy consumption analysis of integrated flowsheets for production of fuel ethanol from lignocellulosic biomass. Enrgy , 31, 2447- 2459

Cole, D. E. (2007)  Issues facing the Auto Industry: Alternative Fuels, Technologies, and Policies ACP Meeting Eagle Crest Conference Center June 20, 2007

Daichendt, M. M., and I. E. Grossmann, (1997) Integration of Hierarchical Decomposition and Mathematical Programming for the Synthesis of Process Flowsheets, Comp. Chem. Eng., 22, 147.

Grossmann, I. E.; Caballero, J. A.; Yeomans, H. (1999) Mathematical Programming Approaches to the Synthesis of Chemical Process Systems”, Korean J. Chem. Eng., 16, 407-426.

Keshwani, D. R., Cheng, J.J. (2009) Switchgrass for bioethanol and other value-added applications: A review Bioresource Technology 100, 1515–1523

Hamelinck, C. N., Geertje van Hooijdonk, G., Faaij, A.P.C. (2005) Ethanol from lignocellulosic biomass: techno-economic performance in short-, middle- and long-termBiomass and Bioenergy 28, 384–410

Huang, J. Qiu, H. and Scott Rozelle, S., (2008), More pain ahead for China's food prices, Far Eastern Economic Review, 171, 5, 8–13.

Huhnke, R. L. (2008) Cellulosic ethanol using gasification-fermentation. Resource: Engineering & Technology for a Sustainable World

http://www.articlearchives.com/energy-utilities/renewable-energy-biomass/896186-1.html

Martín, M.; Ahmetovic, E.;  Grossmann, I.E. (2010) “Optimization of Water Consumption in Second Generation bio-Ethanol Plants ”  I&ECR doi: 10.1021/ie101175p

Martín, M., Grossmann, I.E. (2011) “Energy optimization of lignocellulosic bioethanol production via gasification” accepted AIChE J. | DOI: 10.1002/aic.12544

Phillips, S., Aden, A., Jechura, J. and Dayton, D., Eggeman, T (2007) Thermochemical Ethanol via Indirect Gasification and Mixed Alcohol Synthesis of Lignocellulosic Biomass Technical Report, NREL/TP-510-41168, April 2007

Piccolo, C., Bezzo, F., (2009) A techno-economic comparison between two technologies for bioethanol production from lignocelluloses. Biomass and Bioenergy 33 (2009) 478 – 491

Sun, Y., Cheng, J., (2002) Hydrolysis of lignocellulosic materials for ethanol production: a review. Bioresour. Technol.,  83, 1-11

Zhu, Y., Gerber, M.A., Jones, S.B., Stevens, D.J (2009) Analysis of the effects of compositional and configurational assumptions on product costs for the thermochemical cornversion of lignocellulosic biomass to mixed alcohols  FY 2007 Progress report. DOE PNNL.17949 Revision 1.

 

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[1] Corresponding author. Tel.: +1-412-268-3642; Fax: +1-412-268-7139.

 Email address: grossmann@cmu.edu (I.E. Grossmann)